Volume 8, Issue 2 (9-2021)
Human Information Interaction 2021, 8(2): 12-24 |
Back to browse issues page
Download citation:
BibTeX | RIS | EndNote | Medlars | ProCite | Reference Manager | RefWorks
Send citation to:
BibTeX | RIS | EndNote | Medlars | ProCite | Reference Manager | RefWorks
Send citation to:
Majlesara A, Fahim niya F, Naghshineh N. Proposing a Model for Analyzing Textual Feedback from Users on Social Networks Facing Environmental Events and Actions. Human Information Interaction 2021; 8 (2)
URL: http://hii.khu.ac.ir/article-1-2983-en.html
URL: http://hii.khu.ac.ir/article-1-2983-en.html
University of Tehran
Abstract: (2615 Views)
Background and Objective: The study aims to develop and validate a model for analyzing the textual feedback of users in social networks in the face of environmental events and actions with emphasis on identifying the factors affecting the presentation of text messages by users in social networks.
Research Methodology: Heuristic mixed method has been used. In the first stage, the meta-combined method was applied with a qualitative basis. In the second stage, to inspect, validate the identified factors and prepare the final research model, the survey method via questionnaire and forming conveyor group was combined. Population consisted of: 1) Selection and analysis of written documents related to the analysis of textual feedback and users' feelings, including 60 articles and works based on valid criteria from among 198 articles and works; 2) Experts in information technology, sociology, behaviorism, etc., which 15 people were selected, but as a result and limitations of the corona pandemic comments and suggestions were remotely submitted.
Results: Using the seven steps of meta-combination, a conceptual pattern was obtained in six conceptual layers, categories and codes. In each layer, concepts and topics were included, and to end 27 components were identified. For qualitative validation, the obtained model was found based on the opinions of experts in the form of focus groups and the conceptual model was approved by the research experts.
Conclusion: The conceptual model - obtained from the hybrid stages and focus groups – which has been approved and accepted by experts could be used as a basis for future research to guide, and direct the behavior of users in social networking in order to provide strategies and executive policies for officials and decision makers in relevant organizations and institutions.
Research Methodology: Heuristic mixed method has been used. In the first stage, the meta-combined method was applied with a qualitative basis. In the second stage, to inspect, validate the identified factors and prepare the final research model, the survey method via questionnaire and forming conveyor group was combined. Population consisted of: 1) Selection and analysis of written documents related to the analysis of textual feedback and users' feelings, including 60 articles and works based on valid criteria from among 198 articles and works; 2) Experts in information technology, sociology, behaviorism, etc., which 15 people were selected, but as a result and limitations of the corona pandemic comments and suggestions were remotely submitted.
Results: Using the seven steps of meta-combination, a conceptual pattern was obtained in six conceptual layers, categories and codes. In each layer, concepts and topics were included, and to end 27 components were identified. For qualitative validation, the obtained model was found based on the opinions of experts in the form of focus groups and the conceptual model was approved by the research experts.
Conclusion: The conceptual model - obtained from the hybrid stages and focus groups – which has been approved and accepted by experts could be used as a basis for future research to guide, and direct the behavior of users in social networking in order to provide strategies and executive policies for officials and decision makers in relevant organizations and institutions.
References
1. Alvarez-Milan, Agarzelim, Reto Felix, Philipp A. Rauschnabel, and Christian Hinsch. 2018. "Strategic User engagement: A decision making framework." Journal of Business Research 61-70. doi: [DOI:10.1016/j.jbusres.2018.07.017.]
3. Basiriyan , H. Khaniky, H. (2014). Iranian policymakers and social media policymakers. So-cial Welfare and Development Planning Quarterly, No. 21. (Persian)
4. Basiri.E. Kabiri,A. (2017). Translation is not enough: Comparing Lexicon-based methods for sentiment analysis in Persian.Computer Science and Software Engineering.
6. Barbosa.L., Feng.J., (2010). Robust Sentiment Detection on Twitter from Biased and Noisy Data. Journal of Information Technology.
7. Barnaghi, P., Ghaffari, P., & Breslin, J. G. (2016). Opinion mining and sentiment polarity on twitter and correlation between events and sentiment. In 2016 IEEE Second International Conference on Big Data Computing Service and Applications (BigDataService) (pp. 52-57). IEEE.
9. Basiri, M. E., & Kabiri, A. (2017). Sentence-level sentiment analysis in Persian. 3rd International Conference on Pattern Analysis and Image Analysis, IPRIA 2017, (April), 84-89. [DOI:10.1109/PRIA.2017.7983023.]
11. Bhattacharya, C.B., H. Rao, and M.A. Glynn. 1995. "Understanding the bond of identification: an investigation of its correlates among art museum members." Journal of Marketing 59 (4): 46-57.
13. Bryman, A., and E. Bell. 2007. Business research methods. Oxford University Press.
14. Carlson, Jamie, Jessica Wyllie, Mohammad M. Rahman, and Ranjit Voola. 2018. "Enhancing brand relationship performance through customer participation." Journal of Retailing and Consumer Services 1-9. doi: [DOI:10.1016/j.jretconser.2018.07.008.]
16. Consuegra, D. and Klieb, L. (2018). Impact of Social Media on Consumer Behaviour. International Journal of Information and Decision Sciences, 11(3).
18. Drus, Z. Khalid,H. (2019). Sentiment Analysis in Social Media and Its Application: Systematic Literature Review, Procedia Computer Science.
20. Duwairi, R., & El-Orfali, M. (2014). A study of the effects of preprocessing strategies on sentiment analysis for Arabic text. Journal of Information Science, 40(4), 501-513.
22. Feely, W., Manshadi, M., Frederking, R. E., & Levin, L. S. (2014). The CMU METAL Farsi NLP Approach. In LREC (pp. 4052-4055).
23. Flick, U. 2002. An Introduction to Qualitative Research. London: Sage Publication.
24. Gilmore, J.H., and B.J. II Pine. 2002. "Differentiating hospitality operations via experiences: why selling services is not enough." The Cornell Hotel and Restaurant Administration Quarterly 43 (3): 87-96.
26. Hajli, Nick, Mohana Shanmugam, Savvas Papagiannidis, Debra Zahay, and Marie-Odile Richard. 2017. "Branding co-creation with members of online brand communities." Journal of Business Research 70: 136-144. doi:http://dx.doi.org/10.1016/j.jbusres.2016.08.026.
28. Harmeling.M, Collen, Jordan W.Moffett, Mark J.Arnold, and Brad D.Carlson. 2016. "Toward a theory of user engagement marketing." journal of Academy of Marketing Science 45: 312-335. doi:10.1007/s11747-016-0509-2.
30. Hashim,K.F, Fadhil, N., Engaging with Customer Using Social Media Platform: A Case Study of Malaysia Hotels, Procedia Computer Science.
31. Hollebeek, Linda, Biljana Juric, and Wenyan Tang. 2017. "Virtual brand community engagement practices: a refined typology and model." Journal of Services Marketing 31 (3): pp.204-217. doi: [DOI:10.1108/JSM-01-2016-0006.]
33. Hoseini,P. Ramaki, A., Maleki,H. (2021). SentiPers: A Sentiment Analysis Corpus for Persian, Journal of Information Management.
34. Liu, B. (2012). Sentiment analysis and opinion mining. Synthesis Lectures on Human Language Technologies, 5(1), 1-167
36. Muneta,M., Pascual, C., Lopez,A. 2020, Key Image Attributes to Elicit Likes and Comments on Instagram. Journal of Promotion Management.
37. Pansari, Anitta, and V.Kumar. 2016. "Customer engagement: the construct, antecedents, and consequences." Journal of Academy of Marketing Science 45 (3): 294-311. doi: [DOI:10.1007/s11747-016-0485-6.]
39. Peykary, N. Yaghobi, A. Taheri, H .(2015). Analyzing emotions on Twitter with text mining techniques. International Conference on Web Research. (Persian)
40. Pilan jezhad, M. (2018). Sentiment analysis of social networks users based on text mining. National Conference on New Technologies in Electrical and Computer Engineering. (Persian)
41. Potdar, Vidyasagar, Sujata Joshi, Rahul Harish, Richard Baskerville, and Pornpit Wongthongtham. 2018. "A process model for identifying online customer engagement patterns on." Information Technology & People 31 (2): 595-614. doi: [DOI:10.1108/.]
43. Rezaei, S. Dastkhan, H. Oliya, M. (2018). Text mining methods in analyzing customers' opinions and preferences in social networks: A case study in the Iranian digital products market. (Persian)
44. Rietveld. R., Willemijn,V., (2020). What You Feel, Is What You Like Influence of Message Appeals on Customer Engagement on Instagram.
46. Yue,L., Chen, W., Li,X., Yin,M. A survey of sentiment analysis in social media. Knowledge and Information Systems.
47. Xhema. J. Effect of Social Networks on Consumer Behavior: Complex Buying. (2019). IFAC PapaersOnline 52.
49. Zakeri, R. Zakei, M. Fomani, GH. (2017). The relationship between the use of virtual social networks and the perception of social interactions. Information Technology Quarterly. (Persian)
Send email to the article author
| Rights and permissions | |
 | This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License. |
